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Unescaled data yielded an accuracy of 0.93, with good precision (healthy: 1.00, heart disease: 0.89) but recall needing improvement for healthy patients (0.83). MinMaxScaler showed virtually identical results. StandardScaler significantly improved accuracy to 0.97, exhibiting high precision and recall for both classes (heart disease precision/recall: 0.94/1.00; healthy: 1.00/0.92), and the best F1-score (0.96). Conversely, Normalizer drastically reduced accuracy (0.66) and recall, particularly for healthy patients (0.17), indicating its ineffectiveness for this dataset. StandardScaler emerged as the optimal scaling method.
Without Scaling:
Accuracy: 0.93
Precision: The precision for both classes (healthy = 1.00, heart disease = 0.89) is quite good, indicating that the model is effective in minimizing false positives
Recall: The recall is strong for heart disease (1.00), meaning the model is excellent at identifying heart disease cases However, for healthy patients (0.83), recall could be improved
F1-score: The F1-score is high for both classes, indicating a good balance between precision and recall
With MinMaxScaler:
The performance is very similar to without scaling, with the same accuracy (0.93), precision, recall, and F1-scores
This suggests that MinMaxScaler doesn't significantly impact the model's performance for this particular dataset
With StandardScaler:
Accuracy: 0.97 (higher than the other methods)
Precision and Recall: Both metrics for heart disease are high (precision = 0.94, recall = 1.00), and the precision and recall for healthy patients are also strong (precision = 1.00, recall = 0.92)
F1-score: The F1-score is the highest (0.96), indicating the best balance between precision and recall
This indicates that StandardScaler performed the best in terms of both accuracy and balance between precision and recall, making it the most effective scaling method for this dataset
With Normalizer:
Accuracy: 0.66 (significantly lower than the other models)
Precision: 0.63 for heart disease and 1.00 for healthy patients, but the recall for healthy patients is very low (0.17)
F1-score: The F1-score for heart disease is better (0.77) but still much lower than in other models
Interpretation: The Normalizer caused a substantial drop in performance, especially for the recall of healthy patients, suggesting that this scaling method may not be suitable for this specific task
Summarize English and Arabic text using the statistical algorithm and sorting sentences based on its importance
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